Performance Optimization of Fuel Cell Vehicle Air Compressor Impeller Based on Multi-objective Whale Optimization Algorithm

During recent decades, evolutionary algorithms have been widely studied in optimization problems. The multi-objective whale optimization algorithm based on multi-leader guiding is proposed in this paper, which attempts to offer a proper framework to apply whale optimization algorithm and other swarm intelligence algorithms to solving multi-objective optimization problems. The proposed algorithm adopts several improvements to enhance optimization performance. First, search agents are classified into leadership set and ordinary set by grid mechanism, and multiple leadership solutions guide the population to search the sparse spaces to achieve more homogeneous exploration in per iteration. Second, the differential evolution and whale optimization algorithm are employed to generate the offspring for the leadership and ordinary solutions, respectively. In addition, a novel opposition-based learning strategy is developed to improve the distribution of the initial population. The performance of the proposed algorithm is verified in contrast to 10 classic or state-of-the-arts algorithms on 20 bi-objective and tri-objective unconstrained problems, and experimental results demonstrate the competitive advantages in optimization quality and convergence speed. Moreover, it is tested on load distribution of hot rolling, and the result proves its good performance in real-world applications. Thus, all of the aforementioned experiments have indicated that the proposed algorithm is comparatively effective and efficient.

In the context of Internet of Things (IoT), optimizing quality of service (QoS) parameters is a critical challenge due to its heterogeneous and resource-constrained nature. This paper proposes a novel quantum-inspired multi-objective optimization algorithm for IoT service management. Traditional multi-objective optimization algorithms often face limitations such as slow convergence and susceptibility to local optima, reducing their effectiveness in complex IoT environments. To address these issues, we introduce a quantum-inspired hybrid algorithm that combines the strengths of Multi-Objective Grey Wolf Optimization Algorithm (MOGWOA) and Multi-Objective Whale Optimization Algorithm (MOWOA), enhanced with quantum principles. This novel integration overcomes the limitations of traditional algorithms by improving convergence speed and avoiding local optima. The hybrid algorithm enhances QoS in IoT applications by achieving superior optimization in terms of energy efficiency, latency reduction, convergence, and coverage cost. The incorporation of quantum-inspired mechanisms, such as quantum position and behavior, strengthens the exploration and exploitation capabilities of the algorithm, enabling faster and more accurate optimization. Extensive simulations and testing demonstrate the proposed method’s superior performance compared to existing algorithms, validating its effectiveness in addressing key IoT challenges.

When a high proportion of distributed photovoltaic (PV) systems are integrated into distribution networks, it involves the need for critical load prioritization, which can easily lead to the disconnection of distributed PV or critical loads, causing more severe cascading failures and resulting in issues such as excessive load shedding and supply voltage deviations. To address this, a load shedding control technology for distributed PV distribution networks based on a multi-objective whale optimization algorithm is proposed. The load shedding strategy is optimized using the multi-objective whale optimization algorithm, which continuously updates its position by simulating the whale's preying behavior to find the optimal load shedding strategy within the emergency scenario set, thereby obtaining the optimal solution for load shedding. Experimental results demonstrate that this technology improves both the amount of load shedding and supply voltage deviations, significantly enhancing the stability and economy of the distribution network. It provides a new solution for the integration of distributed PV power sources and load management.

Distributed generation (DG) units are power generating plants that are very important to the architecture of present power system networks. The primary benefits of the addition of these units are to increase the power supply and improve the power quality of a power grid while considering the investment cost and carbon emission cost. Most studies have simultaneously optimized these objectives in a direct way where the objectives are directly infused into the multiobjective framework to produce final values. However, this method may have an unintentional bias towards a particular objective; hence this paper implements a multi-stage framework to handle multiple objectives in a categorical manner to simultaneously integrate DG units and Battery Energy Storage System (BESS) in a distribution network. A new hybrid metaheuristic technique is developed and combined with the Technique Order for Preference by Similarity to Ideal Solution (TOPSIS) approach and the crowding distance technique to produce Pareto optimal solutions from the multiple collective objectives, namely technical, economic, and environmental. Compared to the conventional direct way approach in multiobjective handling, the proposed categorical approach reduces bias towards a set of objective(s) and efficiently handles more objectives. Results also show that the Whale Optimization Algorithm and Genetic Algorithm (WOAGA) produces the smallest power loss of 101.6 kW compared to Whale Optimization Algorithm (WOA) and Genetic Algorithm (GA), which produces 105.1 kW and 105.8 kW respectively. The algorithm, although does not have a faster convergence than the WOA, has a better computational time than the WOA and GA. The multiobjective WOAGA also performs better than the Non-dominating Sorted Genetic Algorithm (NSGA-II) and the multiobjective WOA in terms of the quality of Pareto optimal solutions.

The problem of blood transshipment and allocation in the context of the COVID-19 epidemic has many new characteristics, such as two-stage, trans-regional, and multi-modal transportation. Considering these new characteristics, we propose a novel multi-objective optimization model for the two-stage emergent blood transshipment-allocation. The objectives considered are to optimize the quality of transshipped blood, the satisfaction of blood demand, and the overall cost including shortage penalty. An improved integer encoded hybrid multi-objective whale optimization algorithm (MOWOA) with greedy rules is then designed to solve the model. Numerical experiments demonstrate that our two-stage model is superior to one-stage optimization methods on all objectives. The degree of improvement ranges from 0.69 to 66.26%.

Opposition-based multi-objective whale optimization algorithm with global grid ranking

In this paper, proposed optimization technique called whale optimization algorithm (WOA) is presents to find the optimum allocation of distributed generation (DG) and capacitor in radial distribution systems during reduction of single and multi-objective function namely, (network power losses, voltage deviation, and total operating cost). The multi objective function is formed by the use of weighted sum method. In this paper, multiple-DG units have been analyzed under two load power factors (i.e., unity and optimal) with and without capacitor (C). WOA technique has been applied to a 33-bus radial distribution system. The performance of the WOA technique is compared with other evolutionary optimization methods under different operating conditions of the system. The impact of integrating the proper size of DG and C at the suitable placement based on proposed algorithm are shown in the simulation results.

A 195 cm³ swash-plate piston expander was tested in an ORC using R245fa as working fluid. Rotational speeds ranging from 1000 RPM to 4000 RPM and pressure ratios from 7 to 12 were imposed. In total, 65 steady state points were measured. With these measurements, performance maps were generated to point out the influence of rotational speed and levels of pressure on mechanical power and isentropic efficiency. These maps have highlighted the existence of an optimal rotational speed of around 3000 RPM maximizing the mechanical power, while the speed that maximizes the isentropic efficiency lies between 2000 and 2500 RPM. The maximal mechanical power and isentropic efficiency were 2.8 kW and 53%, respectively. Then the measurements were used to analyze the losses. This analysis has shown that, under the expansion and compression limit, the theoretical isentropic efficiency has values comprised between 90% and 70% for pressure ratios of 7 to 12. The filling factor affects the isentropic efficiency for low rotational speeds and low pressure ratios. Indeed, indicated isentropic efficiency is around 60% for 1000 RPM and around 75% for 4000 RPM. These values stay quite constant with the pressure ratio. Finally, a mechanical efficiency comprised between 40% and 90% was observed, which lowers the isentropic efficiency to values comprised between 30% and 53%. Finally, a model based on energy and mass conservation inside a cylinder volume was successfully calibrated and was able to predict mass flow rate, mechanical power and exhaust temperature with good agreement. This model has enabled desegregation of the influence of pressure drops and leakages on the filling factor, then on the isentropic efficiency. This analysis has shown that pressure drops mainly affect the compactness of the expander, and not so much the isentropic efficiency (except for low rotational speeds where pressure drops can lower the isentropic efficiency by 14%). In contrary, leakages have a strong impact. The importance of the different sources of losses varies with the speed. For the optimal speed of 2500 RPM, under-expansion and compression have the strongest impact, followed by mechanical losses, leakages and pressure drops. respectively.

Whale optimization algorithm (WOA) is a novel-innovative swarm-based meta-heuristic algorithm with excellent performance, but it may still be trapped into local extremum for troublesome problems. To this end, an improved multi-objective whale optimization algorithm (IMOWOA) is proposed to cover the shortages. Firstly, in the search stage of WOA, individual difference is considered to strengthen the exploration ability, and evolution operators are introduced to regenerate the stagnated population to prevent premature convergence. Next, the performance of IMOWOA is compared with MOWOA and other classical optimization algorithms, and a series of multi-objective test functions are used. The results on the convergence and diversity of Pareto front confirm that IMOWOA has better feasibility and competitiveness. Finally, integrated with the least squares support vector regression (LSSVR) model, IMOWOA is applied to the deterministic optimization of vehicle structural crashworthiness. The conclusion testified the efficiency of IMOWOA in the field of vehicle crashworthiness.

In order to solve the problem of the multi-objective spectrum allocation on the joint optimization of maximal network utility and fairness of users in cognitive radio network, based on quantum bee colony theory and membrane computing, a novel multi-objective discrete combinatorial optimization algorithm, named membrane-inspired quantum bee optimization, is proposed. The global optimal solution of single objective can be searched in the elementary membranes, and Pareto front solutions which take account of network utility and fairness, can be obtained from skin membrane with the proposed method. The multi-objective optimization algorithm, which can solve both single objective and multi-objective optimization problems at the same time, is designed by the communication rules between membranes, the cooperative evolution of foraging behavior based on quantum state, and non-dominated sorting. Compared with classical color-sensitive graph coloring algorithm, genetic algorithm, quantum genetic algorithm, and particle swarm optimization under different objective functions, the proposed spectrum allocation method can search the global optimal solution of single objective as shown by the simulation results, and it is superior to classical spectrum allocation algorithms and existing intelligence spectrum allocation methods. The optimal Pareto front solutions of multi-objective spectrum allocation are also obtained.

In this paper, we propose a Multi-objective Five-element Cycle Optimization algorithm based on Multi-strategy fusion (MOFECO-MS) to address the Bi-objective Traveling Thief Problem (BITTP), an extension of the Traveling Thief Problem that incorporates two conflicting objectives. The novelty of our approach lies in a unique individual selection strategy coupled with an innovative element update mechanism rooted in the Five-element Cycle Model. To balance global exploration and local exploitation, the algorithm categorizes the population into distinct groups and applies crossover operations both within and between these groups, while also employing a mutation operator for local searches on the best individuals. This coordinated approach optimizes parameter settings and enhances the search capabilities of the algorithm. Extensive experiments were conducted on nine BITTP instances, comparing MOFECO-MS against eight state-of-the-art multi-objective optimization algorithms. The results show that MOFECO-MS excels in both Hypervolume (HV) and Spread (SP) indicators, while also maintaining a high level of Pure Diversity (PD). Overall, MOFECO-MS outperformed the other algorithms in most instances, demonstrating its superiority and robustness in solving complex multi-objective optimization problems.

In this paper, the problems of fuel cost and emission reduction based on multi-objective optimization are studied and solved. Various multi-objective optimization algorithms, such as the multi-objective ant lion optimization (MOALO), the multi-objective multiverse optimization (MOMVO), the multi-objective particle swarm optimization (MOPSO), the multi-objective thermal exchange optimization (MOTEO), and the multi-objective grasshopper optimization algorithm (MOGOA), are used to reduce the cost and emissions of generation fuel. With equality and inequality restrictions, the suggested work is carried out in an IEEE 30 bus system. To determine the lowest system-wide minimization of generating cost and emission, the results of several optimization strategies are compared. In terms of lowering fuel costs and emissions, the MOGOA performs better than any other multi-objective algorithms.

Whale Optimization Algorithm (WOA) proposed by Seyedali Mirjalili and Andrew Lewis in 2016 is popular and powerful metaheuristic algorithm to search the global solution of optimization problems. WOA is nature-inspired, metaheuristic (randomization and deterministic) algorithm, which has been widely used to solve various single objective, multi objective and multidimensional optimization problems. WOA and its variant have been introduced in engineering applications, bioinformatics, multi-level image segmentation, clustering applications, design of low pass filter, Email classification, Diabetes classification, heterogeneous networks, machine learning etc. WOA is gradient free, easy to represent, capable to explore, exploit the search space and able to avoid local optima. This paper presents overview of WOA, its variants and applications. The performance of WOA is enhanced by introducing hybridization of other methods with WOA such as WOA-PSO, WOA-Levy, WOA-BAT, WOA-ANN, WOA-SVM etc. Objective of metaheuristic algorithm is tofind best position or leader position X* which is near to optimal solution for target prey over successive iteration. Objective function could be based on minimization or maximization approach.

A multi-objective optimization method of cogeneration of power and heat in a combined gas turbine and organic Rankine cycle (ORC) is conducted to achieve the best system design parameters from both thermodynamic and economic aspects by utilizing nondominated sorting genetic algorithm-II (NSGA-II). Exergy efficiency and total cost rate of the system have been considered as objective functions. The cogeneration system consists of a gas turbine (GT) and an organic Rankine cycle (ORC) in which the two cycles are connected through a single-pressure heat recovery steam generator (HRSG). In order to optimize the system, air compressor pressure ratio, air compressor isentropic efficiency, air preheater outlet temperature, turbine inlet temperature, isentropic efficiency of the gas turbine, pinch point temperature of HRSG, pinch point temperature of evaporator, evaporator temperature, and condenser temperature have been selected as decision variables. Optimization results indicate that exergy efficiency of the cycle increases from 51.41% at base case to 55.6% while more than 9.15% reduction is achieved in the total cost rate of the cycle. Also by applying multi-objective optimization, the exergo-economic factor has reached from 10.68 to 27.40.

Exploration of performance optimization strategies for micro-centrifugal compressors in hydrogen fuel cells: A synergistic analysis combining one-dimensional design and three-dimensional flow fields